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        <title>A Long Hard Look at MWEs in the Age of Language Models</title>
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            <title>Proceedings of the 17th Workshop on Multiword Expressions (MWE 2021)</title>
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            <namePart type="given">Paul</namePart>
            <namePart type="family">Cook</namePart>
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        <name type="personal">
            <namePart type="given">Jelena</namePart>
            <namePart type="family">Mitrović</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Carla</namePart>
            <namePart type="given">Parra</namePart>
            <namePart type="family">Escartín</namePart>
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        <name type="personal">
            <namePart type="given">Ashwini</namePart>
            <namePart type="family">Vaidya</namePart>
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            <namePart type="given">Petya</namePart>
            <namePart type="family">Osenova</namePart>
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        <name type="personal">
            <namePart type="given">Shiva</namePart>
            <namePart type="family">Taslimipoor</namePart>
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            <namePart type="given">Carlos</namePart>
            <namePart type="family">Ramisch</namePart>
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    <abstract>In recent years, language models (LMs) have become almost synonymous with NLP. Pre-trained to “read” a large text corpus, such models are useful as both a representation layer as well as a source of world knowledge. But how well do they represent MWEs? This talk will discuss various problems in representing MWEs, and the extent to which LMs address them: \bullet Do LMs capture the implicit relationship between constituents in compositional MWEs (from baby oil through parsley cake to cheeseburger stabbing)? \bullet Do LMs recognize when words are used nonliterally in non-compositional MWEs (e.g. do they know whether there are fleas in the flea market)? \bullet Do LMs know idioms, and can they infer the meaning of new idioms from the context as humans often do?</abstract>
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%0 Conference Proceedings
%T A Long Hard Look at MWEs in the Age of Language Models
%A Shwartz, Vered
%Y Cook, Paul
%Y Mitrović, Jelena
%Y Escartín, Carla Parra
%Y Vaidya, Ashwini
%Y Osenova, Petya
%Y Taslimipoor, Shiva
%Y Ramisch, Carlos
%S Proceedings of the 17th Workshop on Multiword Expressions (MWE 2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F shwartz-2021-long
%X In recent years, language models (LMs) have become almost synonymous with NLP. Pre-trained to “read” a large text corpus, such models are useful as both a representation layer as well as a source of world knowledge. But how well do they represent MWEs? This talk will discuss various problems in representing MWEs, and the extent to which LMs address them: \bullet Do LMs capture the implicit relationship between constituents in compositional MWEs (from baby oil through parsley cake to cheeseburger stabbing)? \bullet Do LMs recognize when words are used nonliterally in non-compositional MWEs (e.g. do they know whether there are fleas in the flea market)? \bullet Do LMs know idioms, and can they infer the meaning of new idioms from the context as humans often do?
%R 10.18653/v1/2021.mwe-1.1
%U https://aclanthology.org/2021.mwe-1.1
%U https://doi.org/10.18653/v1/2021.mwe-1.1
%P 1

Markdown (Informal)

[A Long Hard Look at MWEs in the Age of Language Models](https://aclanthology.org/2021.mwe-1.1) (Shwartz, MWE 2021)

• A Long Hard Look at MWEs in the Age of Language Models (Shwartz, MWE 2021)

ACL

• Vered Shwartz. 2021. A Long Hard Look at MWEs in the Age of Language Models. In Proceedings of the 17th Workshop on Multiword Expressions (MWE 2021), page 1, Online. Association for Computational Linguistics.